Clustering-Based Ensemble of One-Class Classifiers for Hyperspectral Image Segmentation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8480)


In this paper, we propose a new ensemble for an effective segmentation of hyperspectral images. It uses one-class classifiers as base learners. We prove, that despite the multi-class nature of hyperspectral images using one-class approach can be beneficial. One need simply to decompose a multi-class set into a number of simpler one-class tasks. One-class classifiers can handle difficulties embedded in the nature of the hyperspectral data, such as a large number of classes, class imbalance and noisy pixels. For this task, we utilise our novel ensemble, based on soft clustering of the object space. On the basis of each cluster, a weighted one-class classifier is constructed. We show a fast method for calculating weights assigned to each object, and for an automatic calculation of preferred number of clusters. We propose to build such ensemble for each of the classes and then to reconstruct the original multi-class hyperspectral image using Error-Correcting Output Codes. Experimental analysis, carried on a set of benchmark data and backed-up with an extensive statistical analysis, proves that our one-class ensemble is an efficient tool for handling hyperspectral images and outperforms several state-of-the-art binary and multi-class classifiers.


machine learning one-class classification classifier ensemble clustering hyperspectral image image segmentation 


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  1. 1.
    Alpaydin, E.: Combined 5 x 2 cv f test for comparing supervised classification learning algorithms. Neural Computation 11(8), 1885–1892 (1999)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.: Pattern Recognition With Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bicego, M., Figueiredo, M.A.T.: Soft clustering using weighted one-class support vector machines. Pattern Recognition 42(1), 27–32 (2009)CrossRefzbMATHGoogle Scholar
  4. 4.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Cyganek, B.: One-class support vector ensembles for image segmentation and classification. Journal of Mathematical Imaging and Vision 42(2-3), 103–117 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Fauvel, M., Chanussot, J., Benediktsson, J.A.: A spatial-spectral kernel-based approach for the classification of remote-sensing images. Pattern Recognition 45(1), 381–392 (2012)CrossRefGoogle Scholar
  8. 8.
    Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes. Pattern Recognition 44(8), 1761–1776 (2011)CrossRefGoogle Scholar
  9. 9.
    Juszczak, P.: Learning to recognise. A study on one-class classification and active learning. PhD thesis, Delft University of Technology (2006)Google Scholar
  10. 10.
    Koch, M.W., Moya, M.M., Hostetler, L.D., Fogler, R.J.: Cueing, feature discovery, and one-class learning for synthetic aperture radar automatic target recognition. Neural Networks 8(7-8), 1081–1102 (1995)CrossRefGoogle Scholar
  11. 11.
    Krawczyk, B., Woźniak, M.: Combining diverse one-class classifiers. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part II. LNCS, vol. 7209, pp. 590–601. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Krawczyk, B., Woźniak, M.: Diversity measures for one-class classifier ensembles. Neurocomputing 126, 36–44 (2014)CrossRefGoogle Scholar
  13. 13.
    Krawczyk, B., Woźniak, M., Cyganek, B.: Clustering-based ensembles for one-class classification. Information Sciences 264, 182–195 (2014)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Kuncheva, L.I.: Clustering-and-selection model for classifier combination. In: KES, pp. 185–188 (2000)Google Scholar
  15. 15.
    Li, C.-H., Kuo, B.-C., Lin, C.-T., Huang, C.-S.: A spatial-contextual support vector machine for remotely sensed image classification. IEEE Transactions on Geoscience and Remote Sensing 50(3), 784–799 (2012)CrossRefGoogle Scholar
  16. 16.
    Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 50(3), 809–823 (2012)CrossRefGoogle Scholar
  17. 17.
    Li, K., Huang, H., Tian, S.: A novel multi-class svm classifier based on ddag. In: Proceedings of 2002 International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1203–1207 (2002)Google Scholar
  18. 18.
    Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis. An Introduction. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  19. 19.
    Shen, L., Jia, S.: Three-dimensional gabor wavelets for pixel-based hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing 49(12), 5039–5046 (2011)CrossRefGoogle Scholar
  20. 20.
    Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognition 43(7), 2367–2379 (2010)CrossRefzbMATHGoogle Scholar
  21. 21.
    Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learning 54(1), 45–66 (2004)CrossRefzbMATHGoogle Scholar
  22. 22.
    Tax, D.M.J.: Robert P. W. Duin. Characterizing one-class datasets. In: Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp. 21–26 (2005)Google Scholar
  23. 23.
    Wilk, T., Woźniak, M.: Soft computing methods applied to combination of one-class classifiers. Neurocomput. 75, 185–193 (2012)CrossRefGoogle Scholar
  24. 24.
    Zhang, L., Zhou, W., Jiao, L.: Kernel clustering algorithm. Jisuanji Xuebao/Chinese Journal of Computers 25(6), 587–590 (2002)MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWrocław University of TechnologyWrocławPoland
  2. 2.AGH University of Science and TechnologyKrakowPoland

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